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. The position is available from January 2026. This trainee position offers promising researchers an opportunity for academic development through a PhD program. The hired candidate will be admitted to the PhD
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, Molecular Biology or related fields at the time of admittance to the programme Documented experience in at least one of the following areas: microbial growth of non-model bacterial species (ideally in
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15th January 2026 Languages English Norsk Bokmål English English PhD Fellowship in molecular modelling and machine learning for improved subsurface utilization Apply for this job See advertisement
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promising researchers an opportunity for academic development through a PhD education leading to a doctoral degree. The hired candidate will be admitted to the PhD program in Educational Sciences and
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that could provide a basis for successfully completing a doctorate. Applicants that additionally have competence within computational physics/chemistry, geosciences, reservoir engineering or modelling will be
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scientists, and we put emphasis on communicating our research to decision-makers and a wider audience. Our methods include global and regional modeling of human and natural systems, the use of observational
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Deviations” (TOMABOLD), funded by the Research Council of Norway. The PhD position will focus on the large deviation analysis of probabilistic models, and associated problems in PDE, with emphasis
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the interdisciplinary project. Your immediate leader is the supervising Professor, and your Line Manager is Head of Department. Duties of the position To develop risk assessment, models, theory, and visualizations, under
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to include programming and computational modelling as core elements. Deadline 6th January 2026 Employer University of Oslo Municipality Oslo Scope Fulltime (1 positions) Fulltime (%) Duration Project Place of
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funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Job description The University